/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * 
 */

package org.apache.jmeter.visualizers;

import org.apache.jorphan.logging.LoggingManager;
import org.apache.log.Logger;

/*
 * TODO : - implement ImageProducer interface - suggestions ;-)
 */

/**
 * This class implements the representation of an interpolated Spline curve.
 * <P>
 * The curve described by such an object interpolates an arbitrary number of
 * fixed points called <I>nodes</I>. The distance between two nodes should
 * currently be constant. This is about to change in a later version but it can
 * last a while as it's not really needed. Nevertheless, if you need the
 * feature, just <a href="mailto:norguet@bigfoot.com?subject=Spline3eq">write me
 * a note</a> and I'll write it asap.
 * <P>
 * The interpolated Spline curve can't be described by an polynomial analytic
 * equation, the degree of which would be as high as the number of nodes, which
 * would cause extreme oscillations of the curve on the edges.
 * <P>
 * The solution is to split the curve accross a lot of little <I>intervals</I> :
 * an interval starts at one node and ends at the next one. Then, the
 * interpolation is done on each interval, according to the following conditions :
 * <OL>
 * <LI>the interpolated curve is degree 3 : it's a cubic curve ;
 * <LI>the interpolated curve contains the two points delimiting the interval.
 * This condition obviously implies the curve is continuous ;
 * <LI>the interpolated curve has a smooth slope : the curvature has to be the
 * same on the left and the right sides of each node ;
 * <LI>the curvature of the global curve is 0 at both edges.
 * </OL>
 * Every part of the global curve is represented by a cubic (degree-3)
 * polynomial, the coefficients of which have to be computed in order to meet
 * the above conditions.
 * <P>
 * This leads to a n-unknow n-equation system to resolve. One can resolve an
 * equation system by several manners ; this class uses the Jacobi iterative
 * method, particularly well adapted to this situation, as the diagonal of the
 * system matrix is strong compared to the other elements. This implies the
 * algorithm always converges ! This is not the case of the Gauss-Seidel
 * algorithm, which is quite faster (it uses intermediate results of each
 * iteration to speed up the convergence) but it doesn't converge in all the
 * cases or it converges to a wrong value. This is not acceptable and that's why
 * the Jacobi method is safer. Anyway, the gain of speed is about a factor of 3
 * but, for a 100x100 system, it means 10 ms instead of 30 ms, which is a pretty
 * good reason not to explore the question any further :)
 * <P>
 * Here is a little piece of code showing how to use this class :
 * 
 * <PRE> // ... float[] nodes = {3F, 2F, 4F, 1F, 2.5F, 5F, 3F}; Spline3 curve =
 * new Spline3(nodes); // ... public void paint(Graphics g) { int[] plot =
 * curve.getPlots(); for (int i = 1; i < n; i++) { g.drawLine(i - 1, plot[i -
 * 1], i, plot[i]); } } // ...
 * 
 * </PRE>
 * 
 * Have fun with it !<BR>
 * Any comments, feedback, bug reports or suggestions will be <a
 * href="mailto:norguet@bigfoot.com?subject=Spline3">appreciated</a>.
 * 
 * @author <a href="norguet@bigfoot.com">Jean-Pierre Norguet</a>
 * @version $Revison$ updated $Date: 2007-01-07 17:09:09 +0000 (Sun, 07 Jan 2007) $
 */
public class Spline3 {
	private static final Logger log = LoggingManager.getLoggerForClass();

	protected float[][] _coefficients;

	protected float[][] _A;

	protected float[] _B;

	protected float[] _r;

	protected float[] _rS;

	protected int _m; // number of nodes

	protected int _n; // number of non extreme nodes (_m-2)

	final static protected float DEFAULT_PRECISION = (float) 1E-1;

	final static protected int DEFAULT_MAX_ITERATIONS = 100;

	protected float _minPrecision = DEFAULT_PRECISION;

	protected int _maxIterations = DEFAULT_MAX_ITERATIONS;

	/**
	 * Creates a new Spline curve by calculating the coefficients of each part
	 * of the curve, i.e. by resolving the equation system implied by the
	 * interpolation condition on every interval.
	 * 
	 * @param r
	 *            an array of float containing the vertical coordinates of the
	 *            nodes to interpolate ; the vertical coordinates start at 0 and
	 *            are equidistant with a step of 1.
	 */
	public Spline3(float[] r) {
		int n = r.length;

		// the number of nodes is defined by the length of r
		this._m = n;
		// grab the nodes
		this._r = new float[n];
		for (int i = 0; i < n; i++) {
			_r[i] = r[i];
		}
		// the number of non extreme nodes is the number of intervals
		// minus 1, i.e. the length of r minus 2
		this._n = n - 2;
		// computes interpolation coefficients
		try {
			long startTime = System.currentTimeMillis();

			this.interpolation();
			if (log.isDebugEnabled()) {
				long endTime = System.currentTimeMillis();
				long elapsedTime = endTime - startTime;

				log.debug("New Spline curve interpolated in ");
				log.debug(elapsedTime + " ms");
			}
		} catch (Exception e) {
			log.error("Error when interpolating : ", e);
		}

	}

	/**
	 * Computes the coefficients of the Spline interpolated curve, on each
	 * interval. The matrix system to resolve is <CODE>AX=B</CODE>
	 */
	protected void interpolation() {
		// creation of the interpolation structure
		_rS = new float[_m];
		_B = new float[_n];
		_A = new float[_n][_n];
		_coefficients = new float[_n + 1][4];
		// local variables
		int i = 0, j = 0;

		// initialize system structures (just to be safe)
		for (i = 0; i < _n; i++) {
			_B[i] = 0;
			for (j = 0; j < _n; j++) {
				_A[i][j] = 0;
			}
			for (j = 0; j < 4; j++) {
				_coefficients[i][j] = 0;
			}
		}
		for (i = 0; i < _n; i++) {
			_rS[i] = 0;
		}
		// initialize the diagonal of the system matrix (A) to 4
		for (i = 0; i < _n; i++) {
			_A[i][i] = 4;
		}
		// initialize the two minor diagonals of A to 1
		for (i = 1; i < _n; i++) {
			_A[i][i - 1] = 1;
			_A[i - 1][i] = 1;
		}
		// initialize B
		for (i = 0; i < _n; i++) {
			_B[i] = 6 * (_r[i + 2] - 2 * _r[i + 1] + _r[i]);
		}
		// Jacobi system resolving
		this.jacobi(); // results are stored in _rS
		// computes the coefficients (di, ci, bi, ai) from the results
		for (i = 0; i < _n + 1; i++) {
			// di (degree 0)
			_coefficients[i][0] = _r[i];
			// ci (degree 1)
			_coefficients[i][1] = _r[i + 1] - _r[i] - (_rS[i + 1] + 2 * _rS[i]) / 6;
			// bi (degree 2)
			_coefficients[i][2] = _rS[i] / 2;
			// ai (degree 3)
			_coefficients[i][3] = (_rS[i + 1] - _rS[i]) / 6;
		}
	}

	/**
	 * Resolves the equation system by a Jacobi algorithm. The use of the slower
	 * Jacobi algorithm instead of Gauss-Seidel is choosen here because Jacobi
	 * is assured of to be convergent for this particular equation system, as
	 * the system matrix has a strong diagonal.
	 */
	protected void jacobi() {
		// local variables
		int i = 0, j = 0, iterations = 0;
		// intermediate arrays
		float[] newX = new float[_n];
		float[] oldX = new float[_n];

		// Jacobi convergence test
		if (!converge()) {
			if (log.isDebugEnabled()) {
				log.debug("Warning : equation system resolving is unstable");
			}
		}
		// init newX and oldX arrays to 0
		for (i = 0; i < _n; i++) {
			newX[i] = 0;
			oldX[i] = 0;
		}
		// main iteration
		while ((this.precision(oldX, newX) > this._minPrecision) && (iterations < this._maxIterations)) {
			for (i = 0; i < _n; i++) {
				oldX[i] = newX[i];
			}
			for (i = 0; i < _n; i++) {
				newX[i] = _B[i];
				for (j = 0; j < i; j++) {
					newX[i] = newX[i] - (_A[i][j] * oldX[j]);
				}
				for (j = i + 1; j < _n; j++) {
					newX[i] = newX[i] - (_A[i][j] * oldX[j]);
				}
				newX[i] = newX[i] / _A[i][i];
			}
			iterations++;
		}
		if (this.precision(oldX, newX) < this._minPrecision) {
			if (log.isDebugEnabled()) {
				log.debug("Minimal precision (");
				log.debug(this._minPrecision + ") reached after ");
				log.debug(iterations + " iterations");
			}
		} else if (iterations > this._maxIterations) {
			if (log.isDebugEnabled()) {
				log.debug("Maximal number of iterations (");
				log.debug(this._maxIterations + ") reached");
				log.debug("Warning : precision is only ");
				log.debug("" + this.precision(oldX, newX));
				log.debug(", divergence is possible");
			}
		}
		for (i = 0; i < _n; i++) {
			_rS[i + 1] = newX[i];
		}
	}

	/**
	 * Test if the Jacobi resolution of the equation system converges. It's OK
	 * if A has a strong diagonal.
	 */
	protected boolean converge() {
		boolean converge = true;
		int i = 0, j = 0;
		float lineSum = 0F;

		for (i = 0; i < _n; i++) {
			if (converge) {
				lineSum = 0;
				for (j = 0; j < _n; j++) {
					lineSum = lineSum + Math.abs(_A[i][j]);
				}
				lineSum = lineSum - Math.abs(_A[i][i]);
				if (lineSum > Math.abs(_A[i][i])) {
					converge = false;
				}
			}
		}
		return converge;
	}

	/**
	 * Computes the current precision reached.
	 */
	protected float precision(float[] oldX, float[] newX) {
		float N = 0F, D = 0F, erreur = 0F;
		int i = 0;

		for (i = 0; i < _n; i++) {
			N = N + Math.abs(newX[i] - oldX[i]);
			D = D + Math.abs(newX[i]);
		}
		if (D != 0F) {
			erreur = N / D;
		} else {
			erreur = Float.MAX_VALUE;
		}
		return erreur;
	}

	/**
	 * Computes a (vertical) Y-axis value of the global curve.
	 * 
	 * @param t
	 *            abscissa
	 * @return computed ordinate
	 */
	public float value(float t) {
		int i = 0, splineNumber = 0;
		float abscissa = 0F, result = 0F;

		// verify t belongs to the curve (range [0, _m-1])
		if ((t < 0) || (t > (_m - 1))) {
			if (log.isDebugEnabled()) {
				log.debug("Warning : abscissa " + t + " out of bounds [0, " + (_m - 1) + "]");
			}
			// silent error, consider the curve is constant outside its range
			if (t < 0) {
				t = 0;
			} else {
				t = _m - 1;
			}
		}
		// seek the good interval for t and get the piece of curve on it
		splineNumber = (int) Math.floor(t);
		if (t == (_m - 1)) {
			// the upper limit of the curve range belongs by definition
			// to the last interval
			splineNumber--;
		}
		// computes the value of the curve at the pecified abscissa
		// and relative to the beginning of the right piece of Spline curve
		abscissa = t - splineNumber;
		// the polynomial calculation is done by the (fast) Euler method
		for (i = 0; i < 4; i++) {
			result = result * abscissa;
			result = result + _coefficients[splineNumber][3 - i];
		}
		return result;
	}

	/**
	 * Manual check of the curve at the interpolated points.
	 */
	public void debugCheck() {
		int i = 0;

		for (i = 0; i < _m; i++) {
			log.info("Point " + i + " : ");
			log.info(_r[i] + " =? " + value(i));
		}
	}

	/**
	 * Computes drawable plots from the curve for a given draw space. The values
	 * returned are drawable vertically and from the <B>bottom</B> of a Panel.
	 * 
	 * @param width
	 *            width within the plots have to be computed
	 * @param height
	 *            height within the plots are expected to be drawed
	 * @return drawable plots within the limits defined, in an array of int (as
	 *         many int as the value of the <CODE>width</CODE> parameter)
	 */
	public int[] getPlots(int width, int height) {
		int[] plot = new int[width];
		// computes auto-scaling and absolute plots
		float[] y = new float[width];
		float max = java.lang.Integer.MIN_VALUE;
		float min = java.lang.Integer.MAX_VALUE;

		for (int i = 0; i < width; i++) {
			y[i] = value(((float) i) * (_m - 1) / width);
			if (y[i] < min) {
				min = y[i];
			}

			if (y[i] > max) {
				max = y[i];
			}
		}
		if (min < 0) {
			min = 0; // shouldn't draw negative values
		}
		// computes relative auto-scaled plots to fit in the specified area
		for (int i = 0; i < width; i++) {
			plot[i] = Math.round(((y[i] - min) * (height - 1)) / (max - min));
		}
		return plot;
	}

	public void setPrecision(float precision) {
		this._minPrecision = precision;
	}

	public float getPrecision() {
		return this._minPrecision;
	}

	public void setToDefaultPrecision() {
		this._minPrecision = DEFAULT_PRECISION;
	}

	public float getDefaultPrecision() {
		return DEFAULT_PRECISION;
	}

	public void setMaxIterations(int iterations) {
		this._maxIterations = iterations;
	}

	public int getMaxIterations() {
		return this._maxIterations;
	}

	public void setToDefaultMaxIterations() {
		this._maxIterations = DEFAULT_MAX_ITERATIONS;
	}

	public int getDefaultMaxIterations() {
		return DEFAULT_MAX_ITERATIONS;
	}

}
